plotly

We are going to make some plotly plots.

Load packages and data

library(tidyverse)

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6      ✔ purrr   0.3.4 
## ✔ tibble  3.1.8      ✔ dplyr   1.0.10
## ✔ tidyr   1.2.1      ✔ stringr 1.4.1 
## ✔ readr   2.1.2      ✔ forcats 0.5.2 
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

library(p8105.datasets)
library(plotly)

## 
## Attaching package: 'plotly'
## 
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
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## The following object is masked from 'package:stats':
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##     filter
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## The following object is masked from 'package:graphics':
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##     layout

let’s get some data.

data("nyc_airbnb")
nyc_airbnb |> 
    mutate(rating = review_scores_location / 2) |> 
    select(
        borough = neighbourhood_group, neighbourhood, price, room_type,
        lat, long, rating) |> 
    filter(
        borough == "Manhattan",
        room_type == "Entire home/apt",
        price %in% 100:500
        ) |> 
    drop_na(rating)

## # A tibble: 7,568 × 7
##    borough   neighbourhood     price room_type         lat  long rating
##    <chr>     <chr>             <dbl> <chr>           <dbl> <dbl>  <dbl>
##  1 Manhattan Battery Park City   165 Entire home/apt -74.0  40.7    4.5
##  2 Manhattan Battery Park City   225 Entire home/apt -74.0  40.7    5  
##  3 Manhattan Battery Park City   299 Entire home/apt -74.0  40.7    5  
##  4 Manhattan Battery Park City   168 Entire home/apt -74.0  40.7    5  
##  5 Manhattan Battery Park City   100 Entire home/apt -74.0  40.7    5  
##  6 Manhattan Battery Park City   225 Entire home/apt -74.0  40.7    5  
##  7 Manhattan Battery Park City   250 Entire home/apt -74.0  40.7    5  
##  8 Manhattan Battery Park City   110 Entire home/apt -74.0  40.7    4.5
##  9 Manhattan Battery Park City   249 Entire home/apt -74.0  40.7    5  
## 10 Manhattan Battery Park City   325 Entire home/apt -74.0  40.7    5  
## # … with 7,558 more rows

Lets make a scatterplot !!

nyc_airbnb |>
    mutate(
        text_label = str_c("Price: ", price)
    ) |> 
    plot_ly(
        x = ~lat, y = ~long, color = ~ price,
    type = "scatter", mode = "markers", alpha = .5, text = ~text_label
    )

Can we make boxplots? sure can!

nyc_airbnb |>
    mutate(neighbourhood = fct_reorder(neighbourhood, price)) |> 
    plot_ly(
        y = ~price, color = ~neighbourhood,
        type = "box", colors = "viridis")

Can we make a bar plot?

nyc_airbnb |> 
    count(neighbourhood) |>
    mutate(neighbourhood = fct_reorder(neighbourhood, n)) |> 
    plot_ly(x = ~neighbourhood, y = ~n,
            type = "bar")

ggplotly…

ggp_scatterplot = 
    nyc_airbnb |> 
    ggplot(aes(x = lat, y = long, color = price)) +
    geom_point()

ggplotly(ggp_scatterplot)